Motion Model Research Articles

Multi-Degree-of-Freedom Platforms: Development and Outlook

Background: Multi-degree-of-freedom platforms are frequently employed in space docking devices, motion modeling, and robotics. Complex research is being done on multidegree- of-freedom platforms, including work on spatial route creation, control simulation, and forward and backward platform motion solutions. The development of research platforms in their current state is advantageous for advancing multi-degree-of-freedom motion research, which has the potential to advance numerous sectors through new technology. Objective: In this patent study, the benefits and drawbacks of various platforms are explored to analyze the development trend of platforms for academics and engineers through the most recent development of multi-degree-of-freedom platforms. Methods: By using the research methodology, research substance, and inventive structure of the most recent representative patents of the multi-degree-of-freedom platform, the platform's operating principle and characteristics are demonstrated. Results: By contrasting multi-degree-of-freedom platforms in various application domains, the issues with current multi-degree-of-freedom platforms are enumerated, and the platforms' potential future development path and research topics are suggested. Conclusion: The growth of the aviation, medical, and military industries is aided by the development of multi-degree-of-freedom platforms, and the flexible distribution of high-precision parallel platforms has promising future development.

Historische Denkmodelle der Iliosakralbewegungen – Teil 1: Relevanz der Biomechanik des Beckens

This article provides an overview of historical biomechanical models for the sacroiliac joint in osteopathy. In the 1st part, the relevance of the biomechanics of the pelvis for an independent consideration is discussed. The article explains historical views about the influence of pelvic mechanics on the maintenance of normal body function from a static, neurological, visceral, fascial and craniosacral perspective. The role of biomechanical modelling in clinical application is also addressed. In the 2nd part, the core statements of the Fryette-model [1], the Strachan-model [2] and the Mitchell-model [3], [4], [5] will be elaborated and compared with regards to their consistency. Finally, the Mitchell model will be discussed in greater detail since it is the most comprehensive of the presented models for sacroiliac motion.

Analysis of vibration responses in a large airport ground transportation centre caused by maglev and subway trains

This study aims to assess the vibration response of the ground transportation centre (GTC) at an airport integrated transportation hub during the starting and braking processes of underground maglev and subway trains. In this work, we considered the actual motion states of trains entering and exiting the GTC and calculated the traction characteristics of maglev and subway trains. On this basis, the vertical coupling vibration models for a medium- and low-speed maglev train-guideway and a subway train-floating slab track considering variable-speed motion, as well as the vehicle longitudinal motion models, were established, and the vertical and longitudinal fastener forces of the tracks were obtained accordingly. Finally, based on the two-step analysis method, the vertical and longitudinal fastener forces were simultaneously applied to the track–GTC–soil finite element model to evaluate the structural vibrations induced by maglev and subway trains. The results show that the amplitude of the vertical fastener force is an order of magnitude greater than that of the longitudinal fastener force, and the amplitude of the vertical fastener force caused by subway trains is greater than that caused by maglev trains. The starting process of both maglev trains and subway trains results in a greater vertical vibration response of the GTC than the braking process, with the impact of subway trains being greater than that of maglev trains. Within the same area, the vertical vibration level observed at structural column observation points is lower than that at slab observation points. When maglev trains and subway trains start simultaneously, the vibration level response at some points exceeds the specified limit of 75dB.

Excited State Dynamics of Geometrical Evolution of α-Substituted Dibenzoylmethanatoboron Difluoride Complex with Aggregation-Induced Emission Property.

Organic molecules with an aggregation-induced emission (AIE) property have been attracting much attention from the viewpoint of application to solid state emissive materials. For the AIE mechanism, quantum mechanical studies proposed the restriction of the intramolecular motion (RIM) model with the contribution of the conical intersection (CI) and deduced the importance of the restricted access to a conical intersection (RACI) in the potential energy surface (PES). Although these theoretical studies have contributed to the elucidation of AIE phenomena, direct detection of the reaction dynamics is indispensable to clarify the actual PES and the deactivation mechanism. Along this line, we investigated excited state dynamics of the AIE molecule with dibenzoylmethanatoboron difluoride complexes using time-resolved absorption spectroscopies in both visible and infrared (IR) regions. While the reference system of 1,3-bis(4-methoxyphenyl)methanatoboron difluoride (2aBF2) showed strong emission in solution, the methyl-substituted derivative at the α-position of the dioxaborine ring (2amBF2) led to the very weak fluorescence in solution but strong emission in the solid state. Time-resolved visible absorption measurements revealed a peak shift and broadening of the stimulated emission in the solution of 2amBF2, owing to the rapid change of the molecular geometry. With the temporal evolution of time-resolved IR absorption signals and density functional theory (DFT) calculation of these systems, it was deduced that 2amBF2 has two stable geometries, namely, planar and bending, in the S1 state and the bending geometry in the S1 state led to rapid conversion to the S0 state. These results support the RACI model in the aggregated states, leading to the AIE properties.

Bubble Theory and its Applications in Underwater Explosion, Marine Cavitation, and Seismic Exploration

AbstractBubbles play crucial roles in various fields, including naval and ocean engineering, chemical engineering, and biochemical engineering. Numerous theoretical analyses, numerical simulations, and experimental studies have been conducted to reveal the mysteries of bubble motion and its mechanisms. These efforts have significantly advanced research in bubble dynamics, where theoretical study is an efficient method for bubble motion prediction. Since Lord Rayleigh introduced the theoretical model of single-bubble motion in incompressible fluid in 1917, theoretical studies have been pivotal in understanding bubble dynamics. This study provides a comprehensive review of the development and applicability of theoretical studies in bubble dynamics using typical theoretical bubble models across different periods as a focal point and an overview of bubble theory applications in underwater explosion, marine cavitation, and seismic exploration. This study aims to serve as a reference and catalyst for further advancements in theoretical analysis and practical applications of bubble theory across marine fields.

Markerless tracking of tumor and tissues: A motion model approach.

Respiratory motion management is essential in order to achieve high-precision radiotherapy. Markerless motion tracking of tumor can provide a non-invasive way to manage respiratory motion, thereby enhancing treatment accuracy. However, the low contrast in real-time x-ray images for image guidance limits the application of markerless tracking. We present a novel approach based on a motion model to perform markerless tracking of tumor and surrounding tissues even when they have low contrast in real-time x-ray images. A proof-of-concept validation of the method has been performed using digital and physical phantoms at breathing conditions that are significantly different than the planning stage. A motion model is first constructed by performing principal component analysis (PCA) on the planning 4DCT. During treatment, the motion of a surrogate is tracked and used as the input of the motion model, which generates a 3D real-time volume estimation. Such 3D estimation is then projected to 2D to create digitally reconstructed radiographs (DRRs). The relationships between the real-time DRRs, reference DRRs, and reference x-ray images are first established to simulate 2D real-time images from the real-time volume. The registration between the simulated 2D real-time images and real-time x-ray images corrects the initial motion model estimation to ensure the estimated volume matches the real-time condition. In digital phantom, the Dice index of pancreas was improved from 0.74 to 0.78 after correction using real-time DRRs in fully inhaled phase. Validation on lung and pancreas is performed in physical phantom with two motion traces. The surrogate-tumor relationships were intentionally altered to generate large target localization errors due to the differences in body condition between treatment planning stage and during treatment. The real-time correction for the estimated 3D real-time volume was performed using a pair of 2D x-ray images. For the deep breathing motion trace, the tumor localization mean absolute error (MAE) throughout the tracking decreases from around 3mm to less than 1mm after correction. For the shallow breathing motion trace with a 1.7mm baseline shift, the tumor localization MAE throughout the tracking decreases from around 1.5mm to less than 1mm after correction. The method combines the detailed structural information from planning 4DCT and real-time information from real-time x-ray images through a motion model. The matching between the real-time model estimation and 2D real-time images is performed in the same modality so that it can be applied to regions with low contrast in the images. The real-time images successfully corrected the initial motion model estimations in our proof-of-concept validation. This suggests the potential to perform markerless tracking in low-contrast region using a motion model.

Sports biomechanical analysis of knee joint injuries in table tennis players

Table tennis is a fast-paced, explosive sport that puts a lot of strain and stress on players’ lower limbs, particularly their knee joints. This work built a thorough mathematical model to investigate the dynamic and kinematic properties of the knee joint under various motion situations, with the goal of better understanding the biomechanical behavior of the knee joint in table tennis. A model of knee joint motion that takes into account the two degrees of freedom—flexion, extension, and rotation—is put forth based on the concepts of human biomechanics and kinematics theory. This model uses the Newton Euler equation to explain the mechanical behavior of the knee joint and integrates internal forces like muscle forces and external factors like ground reaction forces for torque balance analysis. This study offers a thorough examination of the force distribution and knee joint trajectory in table tennis using numerical simulation techniques. The findings show that the knee joint undergoes considerable compression and shear forces during intense activity, and that the joint’s stress properties vary significantly depending on the kind of movement. This finding is a valuable resource for table tennis players’ knee joint injury prevention and rehabilitation.

Power Brownian motion: an Ornstein-Uhlenbeck lookout

Abstract The well-known Ornstein-Uhlenbeck process (OUP) is the central go-to Gaussian model for statistical-equilibrium processes. The recently-introduced power Brownian motion (PBM) is a Gaussian model for diffusive motions, regular and anomalous alike. Using the Lamperti transform, this paper establishes PBM as the ‘diffusion counterpart’ of the OUP. Namely, the paper shows that PBM is for diffusive motions what the OUP is for statistical-equilibrium processes. The intimate parallels between the OUP and PBM are explored and illuminated via four main perspectives. 1) Statistical characterizations. 2) Kernel-integration with respect to Gaussian white noise. 3) Spatio-temporal scaling of the Wiener process. 4) Langevin stochastic dynamics driven by Gaussian white noise. To date, the prominent Gaussian models for anomalous diffusion are fractional Brownian motion (FBM), and scaled Brownian Motion (SBM). Due to its intimate OUP parallels, due to the ‘anomalous features’ it displays, due to the fact that it encompasses SBM, and following a detailed comparison to FBM: this paper argues the case for PBM as a central go-to Gaussian model for regular and anomalous diffusion.

Design of optimum spacecraft reorientation control with a combined criteria of quality based on the quaternions

Specifical original problem of attitude controlling for spacecraft was proposed in this paper. Problem of optimal rotation from a known initial state in a prescribed spatial orientation was studied in detail (turnaround time is not fixed). Design of optimal program of reorientation is based on new indicator of quality that combines energy costs including the contribution of controlling torques and integral of rotary energy (in a known proportion) and reorientation time; presence of duration factor bounds time of rotation finish. To construct an optimal control of angular momentum changing, quaternionic method and the maximum principle were applied. Differential equation that relates spacecraft angular momentum and quaternion of spacecraft orientation is a base to obtain analytic solution to a problem. We reveal the properties of optimum control program analytically, and study key features of optimum motion in details. Also, we write the formalized equations, mathematical formulas to design optimal law for change of spacecraft’s angular momentum. Analytic relations and equations are given for finding the optimal solution. Control law (in as explicit dependence between phase variables and con-trolling variables has been formulated. Main relations determining optimum values of parameters for rotation control algorithm were given. The closed-form law for rotation was obtained for dynamically symmetric solids. Numerical example as well as results of mathematical modeling of spacecraft motion that formed using optimum control are presented. This data as addition to the made theoretic descriptions shows reorientation process (in virtual form) and demonstrates practical feasibility of the developed control method. A designed algorithm for optimal control of rotation improves an efficiency of attitude system, and originates more economical performing of space vehicle during its flight along orbit.

An ADCP Attitude Dynamic Errors Correction Method Based on Angular Velocity Tensor and Radius Vector Estimation

An acoustic Doppler current profiler (ADCP) installed on a platform produces rotational tangential velocity as a result of variations in the platform’s attitude, with both the tangential velocity and radial orientation varying between each pulse’s transmission and reception by the transducer. These factors introduce errors into the measurements of vessel velocity and flow velocity. In this study, we address the errors induced by dynamic factors related to variations in attitude and propose an ADCP attitude dynamic error correction method based on angular velocity tensor and radius vector estimation. This method utilizes a low-sampling-rate inclinometer and compass data and estimates the angular velocity tensor based on a physical model of vessel motion combined with nonlinear least-squares estimation. The angular velocity tensor is then used to estimate the transducers’ radius vectors. Finally, the radius vectors are employed to correct the instantaneous tangential velocity within the measured velocities of the vessel and flow. To verify the effectiveness of the proposed method, field tests were conducted in a water pool. The results demonstrate that the proposed method surpasses the attitude static correction approach. In comparison with the ASC method, the average relative error in vessel velocity during free-swaying movement decreased by 20.94%, while the relative standard deviation of the error was reduced by 17.38%.

Evade Unknown Pursuer via Pursuit Strategy Identification and Model Reference Policy Adaptation (MRPA) Algorithm

The game of pursuit–evasion has always been a popular research subject in the field of Unmanned Aerial Vehicles (UAVs). Current evasion decision making based on reinforcement learning is generally trained only for specific pursuers, and it has limited performance for evading unknown pursuers and exhibits poor generalizability. To enhance the ability of an evasion policy learned by reinforcement learning (RL) to evade unknown pursuers, this paper proposes a pursuit UAV attitude estimation and pursuit strategy identification method and a Model Reference Policy Adaptation (MRPA) algorithm. Firstly, this paper constructs a Markov decision model for the pursuit–evasion game of UAVs that includes the pursuer’s attitude and trains an evasion policy for a specific pursuit strategy using the Soft Actor–Critic (SAC) algorithm. Secondly, this paper establishes a novel relative motion model of UAVs in pursuit–evasion games under the assumption that proportional guidance is used as the pursuit strategy, based on which the pursuit UAV attitude estimation and pursuit strategy identification algorithm is proposed to provide adequate information for decision making and policy adaptation. Furthermore, a Model Reference Policy Adaptation (MRPA) algorithm is presented to improve the generalizability of the evasion policy trained by RL in certain environments. Finally, various numerical simulations imply the precision of pursuit UAV attitude estimation and the accuracy of pursuit strategy identification. Also, the ablation experiment verifies that the MRPA algorithm can effectively enhance the performance of the evasion policy to deal with unknown pursuers.

Object detection and tracking for drones: A system design using dynamic visual SLAM

Abstract. Drones can play a quite crucial role in many walks of life today. Enhancing the visual perception ability of drones is crucial to their intelligence level. Among them, it is necessary to focus on strengthening the detection, tracking and mapping capabilities of drones for dynamic objects. However, the existing visual SLAM systems carried by drones do not perform well in dynamic environments. This project designs a monocular visual SLAM system specifically for drones, aiming to achieve efficient three-dimensional mapping and target tracking, surpassing the limitations of simple static mapping and positioning. Besides, this project constructs a drone dynamic SLAM system developed on the ORB-SLAM3 structure, uses drone images to detect, track and map object motion models, and reconstructs environmental maps to obtain motion parameters with real physical scales. This project strives to optimize the input pre-processing module, improve the validity of data and output environmental maps and raster maps. The outcomes demonstrate the system's strong accuracy and adaptability in dynamic installation procedures.

Improved DV-Hop algorithm based on geometric Brownian motion model under communication interference

Abstract In a mobile sensor network, a traditional positioning algorithm is unable to locate unknown nodes when losing anchor positions caused by communication interference. To solve this problem, an improved DV-Hop algorithm based on a geometric Brownian motion (GBM) model was proposed including two main stages: location of sink node (LSN) and location of blind node (LBN). In the LSN stage, if the signal transmission of anchors is normal, the GBM model records the moving positions of the anchors. If not, the GBM model predicts the estimated average positions of the anchors using recorded data. Then, the trial count of the GBM model is optimized to further improve the prediction accuracy and computational overhead. In the LBN stage, the unknown nodes’ positions are obtained by the DV-Hop algorithm. In a traditional DV-Hop algorithm, the approximate minimum hop number and average hop distance may lead to huge deviation between true position and estimated position. To improve the positioning accuracy in the LBN stage, the strategies of multi-communication radius and hop distance weighting were adopted. The simulation results demonstrated that the proposed algorithm has the capability to resist communication interference and adaptability at different node speeds , maintaining a relatively high accuracy in locating unknown nodes.

On the Optimal Distribution of Traction Forces in Cable Propulsors of Mobile Robots

The analysis of methods of distribution of traction forces in various propellers of mobile robotic complexes and transport vehicles is carried out. The problem of synthesizing a method for controlling the distribution of the total traction load between interconnected electric drives of mobile robot propellers, discretely interacting with the support surface, is considered. An underwater mobile robot is simulated with several "walking-like" anchor-cable propellers, which ensure the movement of the underwater mobile robot by pulling the body to the supports located on the bottom. A mathematical model of the rectilinear motion of a mobile robotic device with walking-type propellers has been compiled. A mathematical description of the electric drive of the propulsion of such a mobile robot is proposed, taking into account its kinetic transfer function. It is shown that the total traction effort realized by a mobile robot is the source of the moment of resistance in the electric drive of each mover. In this regard, coefficients characterizing the distribution of traction force have been introduced into the differential equation of electric drives. A feature of the functions of these coefficients is their dependence on the distance traveled, speed and strength of resistance to movement. To optimize the distribution of the total power load between the thrusters, a target functional has been compiled. It is shown that as such a target functional, a requirement for a minimum of total heat losses in an interconnected electric drive of propellers can be formulated. To find the minimum of the accepted functional, the Euler-Poisson equations are compiled. As an additional limitation, the condition of physical feasibility is introduced. The results of solving such an optimization problem are presented on the simplest calculation scheme of two DC electric drives, between which the load is distributed along the rectilinear movement of a solid. As a result of solving the formulated optimization problem, the dependences of control actions for electric drives (voltage for DC electric drives), graphs of changes in the target function providing optimal distribution of traction forces between them are obtained, and the optimality of such distribution is proven.

Survey of learning-based approaches for robotic in-hand manipulation

Human dexterity is an invaluable capability for precise manipulation of objects in complex tasks. The capability of robots to similarly grasp and perform in-hand manipulation of objects is critical for their use in the ever changing human environment, and for their ability to replace manpower. In recent decades, significant effort has been put in order to enable in-hand manipulation capabilities to robotic systems. Initial robotic manipulators followed carefully programmed paths, while later attempts provided a solution based on analytical modeling of motion and contact. However, these have failed to provide practical solutions due to inability to cope with complex environments and uncertainties. Therefore, the effort has shifted to learning-based approaches where data is collected from the real world or through a simulation, during repeated attempts to complete various tasks. The vast majority of learning approaches focused on learning data-based models that describe the system to some extent or Reinforcement Learning (RL). RL, in particular, has seen growing interest due to the remarkable ability to generate solutions to problems with minimal human guidance. In this survey paper, we track the developments of learning approaches for in-hand manipulations and, explore the challenges and opportunities. This survey is designed both as an introduction for novices in the field with a glossary of terms as well as a guide of novel advances for advanced practitioners.

Ground motion model for acceleration response spectra using conditional-generative adversarial network

Ground motion model for acceleration response spectra using conditional-generative adversarial network

A Decision Model for Ship Overtaking in Straight Waterway Channels

Overtaking situations are commonly encountered in maritime navigation, and the overtaking process involves various risk factors that significantly contribute to collision incidents. It is crucial to conduct research on the maneuvering behaviors and decision-making processes associated with ship overtaking. This paper proposes a method based on the analysis of ship maneuvering performance to investigate overtaking behaviors in navigational channels. A relative motion model is established for both the overtaking and the overtaken vessels, and the inter-vessel distance is calculated, taking into account the psychological perceptions of the ship’s driver. A decision-making model for ship overtaking is presented to provide a safety protocol for overtaking maneuvers. Applying this method to overtaking data from the South Channel shows that it effectively characterizes both the permissible overtaking space and the driver’s overtaking desire. Additionally, it enables the prediction of optimal overtaking timing and strategies based on short-term trajectory forecasts. Thus, this method not only offers a safe overtaking plan for vessels but also provides auxiliary information for decision making in intelligent ship navigation.

Optimized Investment Strategy and Proportional Reinsurance of Insurance Company under Ornstein–Uhlenbeck and Geometric Brownian motion Models

Optimized Investment Strategy and Proportional Reinsurance of Insurance Company under Ornstein–Uhlenbeck and Geometric Brownian motion Models

Rollover prevention by designing a new fuzzy set solution for automotive active stabilizer bars based on a complex dynamic model

In this paper, the author introduces using active stabilizer bars (hydraulic anti-roll bars) for automobiles to improve the stability of rolling over when an automobile is steering. The car rollover oscillation is described by a complex dynamic model, a combination of the Pacejka tire model, the motion model, and the full oscillation model. A new fuzzy solution has been developed in order to control the active stability bars. This algorithm has three inputs related to the car’s rollover factor. The fuzzy rule (with 125 situations) and membership function are determined based on views related to the car’s stability and the antiroll system’s responsiveness. Numerical calculations and simulation are performed with two cases corresponding to two kinds of steering angles. Three situations correspond to three velocity values: v1, v2, and v3, and four scenarios are simulated in each situation to facilitate the comparison of results. According to the research findings, output values such as roll angle, rollover index, and load change are drastically reduced when the new fuzzy solution is applied to the active anti-roll bars. In the last case, the rollover occurs when the automobile does not use any stability bars or uses regular mechanical bars. The roll angle peak values reach 8.79° and 10.79°, while the maximum value belonging to the Active with Fuzzy situation is 10.94° without rolling over (corresponding to a rollover index of 0.64). The results obtained from this paper are the basis for developing more complex solutions for stabilizer bars in the future.

Predictive Modeling of Semi-Submersible Floater Motion Using Bi-LSTM Model

Abstract Floating offshore wind turbines (FOWTs) are emerging as a promising renewable energy solution, tapping into the vast wind resources in deep-sea locations. Accurate predictions of heave, pitch, sway, roll, yaw, and surge are essential to ensuring the structural integrity and power generation efficiency of FOWTs. Traditional methods relying on potential flow theory to predict FOWTs’ hydrodynamic responses involve simplifications that may not capture real-world complexities. Although computational fluid dynamics (CFD) simulations offer accuracy, they are computationally expensive. This study explores the use of Bi-directional long-short-term memory (Bi-LSTM) neural networks as an alternative to address computational challenges in predicting FOWTs’ hydrodynamic responses. These networks excel at handling time series data, capturing temporal correlations crucial for understanding FOWT floater motion dynamics. They also effectively capture bidirectional dependencies, enhancing their suitability for this application. The model considers various input parameters, including wave amplitudes, periods, steepness, directions, and floater design characteristics such as stifness, pre-tension, and the free length of mooring lines. The Bi-LSTM model is trained using a subset of the CFD dataset generated for the scaled OC5 semi-submersible platform with mooring, reserving the remainder for validation and testing. The comparison between simulated and predicted motion demonstrated a strong correlation in surge but noticeable discrepancies in heave and pitch. FFT analysis highlighted consistent frequency components but variations in magnitude, suggesting areas for model sensitivity improvement and further investigation into wave interaction dynamics. The correlation coefficients revealed a strong positive correlation for the surge motion, while the heave and pitch motions showed slightly lower correlation coefficients, suggesting better performance in predicting the surge.